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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Cross-spectral Image Patch Similarity using Convolutional Neural Network. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–5).
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Learning to Colorize Infrared Images. In 15th International Conference on Practical Applications of Agents and Multi-Agent Systems.
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Cristhian A. Aguilera, Xaver Soria, Angel D. Sappa, & Ricardo Toledo. (2017). RGBN Multispectral Images: a Novel Color Restoration Approach. In 15th International Conference on Practical Applications of Agents and Multi-Agent Systems (Vol. 619, pp. 155–163).
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Angel J. Valencia, Roger M. Idrovo, Angel D. Sappa, Douglas Plaza G., & Daniel Ochoa. (2017). A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–6).
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Dennis G. Romero, Roberto Yoncon, Angel Guale, Bonny Bayot, & Fanny Panchana. (2017). Evaluación de técnicas de clasificación orientadas a la identificación automática de órganos del camarón a partir de imágenes histológicas. In 15th LACCEI International Multi-Conference for Engineering, Education, and Technology (Vol. 2017-July, pp. 1–6).
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Infrared Image Colorization based on a Triplet DCGAN Architecture. In 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) (Vol. 2017-July, pp. 212–217).
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Angel D. Sappa, Juan A. Carvajal, Cristhian A. Aguilera, Miguel Oliveira, Dennis G. Romero, & Boris X. Vintimilla. (2016). Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study. Sensors Journal, Vol. 16, pp. 1–15.
Abstract: This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR).
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Cristhian A. Aguilera, Francisco J. Aguilera, Angel D. Sappa, & Ricardo Toledo. (2016). Learning crossspectral similarity measures with deep convolutional neural networks. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 267–275).
Abstract: The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains.
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Miguel Oliveira, Vítor Santos, Angel D. Sappa, Paulo Dias, & A. Paulo Moreira. (2016). Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives. Robotics and Autonomous Systems Journal, Vol. 83, pp. 312–325.
Abstract: When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.
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Miguel Oliveira, Vítor Santos, Angel D. Sappa, Paulo Dias, & A. Paulo Moreira. (2016). Incremental Texture Mapping for Autonomous Driving. Robotics and Autonomous Systems Journal, Vol. 84, pp. 113–128.
Abstract: Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
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